This paper introduces an incremental method for "structure from motion" of complex scenes from a video sequence. More precisely, we estimate the 3D positions of the viewed points in images and the camera pos...
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This paper introduces an incremental method for "structure from motion" of complex scenes from a video sequence. More precisely, we estimate the 3D positions of the viewed points in images and the camera positions and orientations through the sequence. The method can be seen as a fast but accurate alternative to classical reconstruction methods that use bundle adjustment, and that can become slow and computation time expensive for very long scenes. Our results are compared to the reconstruction obtained by the classical hierarchical bundle adjustment method. They have also been successfully used as a reference sequence for the vision based localization of an autonomous mobile robot
This paper describes a new vision based method for the simultaneous localization and mapping of mobile robots. The only data used is a video input from a moving calibrated monocular camera. From the detection and matc...
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This paper describes a new vision based method for the simultaneous localization and mapping of mobile robots. The only data used is a video input from a moving calibrated monocular camera. From the detection and matching of interest points in images at video rate, robust estimates of the camera poses are computed in real-time and a 3D map of the environment is reconstructed. The computed 3D structure is constantly refined thanks to the introduction of a fast and local bundle adjustment method that makes this approach particularly accurate and reliable. Actually, this method can be seen as a new visual tool that may be used in conjunction with usual systems (GPS, inertia sensors, etc) in SLAM applications
We describe an innovative system designed for museums to create, manage and present multimedia based representations of museum artefacts in virtual exhibitions both inside and outside museums. Our system creates digit...
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We optimize the noise shaping behavior of color error diffusion by designing an optimized error filter based on a proposed noise shaping model for color error diffusion and a generalized linear spatially-invariant mod...
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We optimize the noise shaping behavior of color error diffusion by designing an optimized error filter based on a proposed noise shaping model for color error diffusion and a generalized linear spatially-invariant model of the human visual system. Our approach allows the error filter to have matrix-valued coefficients and diffuse quantization error across channels in an opponent color representation. Thus, the noise is shaped into frequency regions of reduced human color sensitivity. To obtain the optimal filter, we derive a matrix version of the Yule-Walker equations which we solve by using a gradient descent algorithm.
Error diffusion halftoning is a popular method of producing frequency modulated (FM) halftones. In FM halftoning the dot size and shape is fixed (equal to one pixel) and the dot frequency is varied in accordance to th...
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Error diffusion halftoning is a popular method of producing frequency modulated (FM) halftones. In FM halftoning the dot size and shape is fixed (equal to one pixel) and the dot frequency is varied in accordance to the graylevel values of the underlying grayscale image. We generalize error diffusion to produce FM halftones with user controlled dot size and shape using block quantization and a block filter in the feedback loop. We call this modified quantization and feedback process block error diffusion. The block filters are designed from well known scalar error filter prototypes and retain their properties. Further, we show that choosing a structured block filter results in an efficient parallel implementation of block error diffusion.
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